Methods and apparatus for orientation keypoints for complete 3d human pose computerized estimation

ABSTRACT

Embodiments of the present invention describe a system that receives an image depicting at least one subject, predicts at least one orientation keypoint associated with a section of the body part of the at least one subject via a neural network detector and determines a three-axis joint rotation associated with the section of the body part of the at least one subject based on at least one orientation keypoint associated with the body part of the at least one subject and at least one joint keypoint associated with the body part of the at least one subject. Orientation keypoints can improve the estimation of an associated joint keypoints, dense pose correspondence and landmark.

FIELD OF TECHNOLOGY

The present disclosure generally relates to computer-based systemsconfigured for one or more technological computer-based applications andmethods for computerized estimation of orientation keypoints forcomplete 3D human poses.

BACKGROUND OF TECHNOLOGY

Human pose keypoints are typically defined as the major joint positionson the human skeleton. These keypoints can correspond to major skeletaljoints, and can include features such as eyes, ears or nose. Identifyingand separating the keypoint mappings for multi-person images withoutmixing body parts from different individuals is a complex problem.Single (Red, Green, Blue) RGB images and videos lack depth information,and images in the wild lack scale information or skeletal measurements.While 2D images can be annotated with 2D keypoints, computing 3Dkeypoint data is a more complex problem in part because these keypointslack important skeletal rotation information.

SUMMARY

At least one embodiment described herein includes a system to localizehuman joints and solve for 3D human poses in terms of both position andfull three-axis rotations using at least one image frame. In someembodiments, the system is enabled by a neural network detector thatpredicts the 3D location of a full set of orientation keypoints. In someembodiments the system predicts a position associated with the at leastone subject, size associated with the at least one subject, and amovement associated with the at least one subject.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the present disclosure can be further explainedwith reference to the attached drawings, wherein like structures arereferred to by like numerals throughout the several views. The drawingsshown are not necessarily to scale, with emphasis instead generallybeing placed upon illustrating the principles of the present disclosure.Therefore, specific structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a representativebasis for teaching one skilled in the art to variously employ one ormore illustrative embodiments.

FIGS. 1-6 show one or more schematic flow diagrams, certaincomputer-based architectures, and/or screenshots of various specializedgraphical user interfaces which are illustrative of some exemplaryaspects of at least some embodiments of the present disclosure.

DETAILED DESCRIPTION

Various detailed embodiments of the present disclosure, taken inconjunction with the accompanying figures, are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely illustrative. In addition, each of the examples given inconnection with the various embodiments of the present disclosure isintended to be illustrative, and not restrictive.

Throughout the specification, the following terms take the meaningsexplicitly associated herein, unless the context clearly dictatesotherwise. The phrases “in one embodiment” and “in some embodiments” asused herein do not necessarily refer to the same embodiment(s), thoughit may. Furthermore, the phrases “in another embodiment” and “in someother embodiments” as used herein do not necessarily refer to adifferent embodiment, although it may. Thus, as described below, variousembodiments may be readily combined, without departing from the scope orspirit of the present disclosure.

In addition, the term “based on” is not exclusive and allows for beingbased on additional factors not described, unless the context clearlydictates otherwise. In addition, throughout the specification, themeaning of “a,” “an,” and “the” include plural references. The meaningof “in” includes “in” and “on.”

It is understood that at least one aspect/functionality of variousembodiments described herein can be performed in real-time and/ordynamically. As used herein, the term “real-time” is directed to anevent/action that can occur instantaneously or almost instantaneously intime when another event/action has occurred. For example, the “real-timeprocessing,” “real-time computation,” and “real-time execution” allpertain to the performance of a computation during the actual time thatthe related physical process (e.g., a user interacting with anapplication on a mobile device) occurs, in order that results of thecomputation can be used in guiding the physical process.

As used herein, the term “dynamically” and term “automatically,” andtheir logical and/or linguistic relatives and/or derivatives, mean thatcertain events and/or actions can be triggered and/or occur without anyhuman intervention. In some embodiments, events and/or actions inaccordance with the present disclosure can be in real-time and/or basedon a predetermined periodicity of at least one of: nanosecond, severalnanoseconds, millisecond, several milliseconds, second, several seconds,minute, several minutes, hourly, several hours, daily, several days,weekly, monthly, etc.

As used herein, the term “runtime” corresponds to any behavior that isdynamically determined during an execution of a software application orat least a portion of software application.

In some embodiments, exemplary inventive, specially programmed computingsystems/platforms with associated devices are configured to operate inthe distributed network environment, communicating with one another overone or more suitable data communication networks (e.g., the Internet,satellite, etc.) and utilizing one or more suitable data communicationprotocols/modes such as, witho ut limitation, IPX/SPX, X.25, AX.25,AppleTalk(™), TCP/IP (e.g., HTTP), near-field wireless communication(NFC), RFID, Narrow Band Internet of Things (NBIOT), 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and other suitablecommunication modes. In some embodiments, the NFC can represent ashort-range wireless communications technology in which NFC-enableddevices are “swiped,” “bumped,” “tap” or otherwise moved in closeproximity to communicate. In some embodiments, the NFC could include aset of short-range wireless technologies, typically requiring a distanceof 10 cm or less. In some embodiments, the NFC may operate at 13.56 MHzon ISO/IEC 18000-3 air interface and at rates ranging from 106 kbit/s to424 kbit/s. In some embodiments, the NFC can involve an initiator and atarget; the initiator actively generates an RF field that can power apassive target. In some embodiment, this can enable NFC targets to takevery simple form factors such as tags, stickers, key fobs, or cards thatdo not require batteries. In some embodiments, the NFC's peer-to-peercommunication can be conducted when a plurality of NFC-enable devices(e.g., smartphones) within close proximity of each other.

The material disclosed herein may be implemented in software or firmwareor a combination of them or as instructions stored on a machine-readablemedium, which may be read and executed by one or more processors. Amachine-readable medium may include any medium and/or mechanism forstoring or transmitting information in a form readable by a machine(e.g., a computing device). For example, a machine-readable medium mayinclude read only memory (ROM); random access memory (RAM); magneticdisk storage media; optical storage media; flash memory devices;electrical, optical, acoustical or other forms of propagated signals(e.g., carrier waves, infrared signals, digital signals, etc.), andothers.

As used herein, the terms “computer engine” and “engine” identify atleast one software component and/or a combination of at least onesoftware component and at least one hardware component which aredesigned/programmed/configured to manage/control other software and/orhardware components (such as the libraries, software development kits(SDKs), objects, etc.).

Examples of hardware elements may include processors, microprocessors,circuits, circuit elements (e.g., transistors, resistors, capacitors,inductors, and so forth), integrated circuits, application specificintegrated circuits (ASIC), programmable logic devices (PLD), digitalsignal processors (DSP), field programmable gate array (FPGA), logicgates, registers, semiconductor device, chips, microchips, chip sets,and so forth. In some embodiments, the one or more processors may beimplemented as a Complex Instruction Set Computer (CISC) or ReducedInstruction Set Computer (RISC) processors; x86 instruction setcompatible processors, multi-core, or any other microprocessor orcentral processing unit (CPU). In various implementations, the one ormore processors may be dual-core processor(s), dual-core mobileprocessor(s), and so forth.

Examples of software may include software components, programs,applications, computer programs, application programs, system programs,machine programs, operating system software, middleware, firmware,software modules, routines, subroutines, functions, methods, procedures,software interfaces, application program interfaces (API), instructionsets, computing code, computer code, code segments, computer codesegments, words, values, symbols, or any combination thereof.Determining whether an embodiment is implemented using hardware elementsand/or software elements may vary in accordance with any number offactors, such as desired computational rate, power levels, heattolerances, processing cycle budget, input data rates, output datarates, memory resources, data bus speeds and other design or performanceconstraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that make the logic or processor. Of note, various embodimentsdescribed herein may, of course, be implemented using any appropriatehardware and/or computing software languages (e.g., C++, Objective-C,Swift, Java, JavaScript, Python, Perl, QT, etc.).

In some embodiments, one or more of exemplary inventive computer-basedsystems of the present disclosure may include or be incorporated,partially or entirely into at least one personal computer (PC), laptopcomputer, ultra-laptop computer, tablet, touch pad, portable computer,handheld computer, palmtop computer, personal digital assistant (PDA),cellular telephone, combination cellular telephone/PDA, television,smart device (e.g., smart phone, smart tablet or smart television),mobile internet device (MID), messaging device, data communicationdevice, and so forth.

As used herein, term “server” should be understood to refer to a servicepoint which provides processing, database, and communication facilities.By way of example, and not limitation, the term “server” can refer to asingle, physical processor with associated communications and datastorage and database facilities, or it can refer to a networked orclustered complex of processors and associated network and storagedevices, as well as operating software and one or more database systemsand application software that support the services provided by theserver. Cloud servers are examples.

In some embodiments, as detailed herein, one or more of exemplaryinventive computer-based systems of the present disclosure may obtain,manipulate, transfer, store, transform, generate, and/or output anydigital object and/or data unit (e.g., from inside and/or outside of aparticular application) that can be in any suitable form such as,without limitation, a file, a contact, a task, an email, a tweet, a map,an entire application (e.g., a calculator), etc. In some embodiments, asdetailed herein, one or more of exemplary inventive computer-basedsystems of the present disclosure may be implemented across one or moreof various computer platforms such as, but not limited to: (1) AmigaOS,AmigaOS 4, (2) FreeBSD, NetBSD, OpenBSD, (3) Linux, (4) MicrosoftWindows, (5) OpenVMS, (6) OS X (Mac OS), (7) OS/2, (8) Solaris, (9)Tru64 UNIX, (10) VM, (11) Android, (12) Bada, (13) BlackBerry OS, (14)Firefox OS, (15) iOS, (16) Embedded Linux, (17) Palm OS, (18) Symbian,(19) Tizen, (20) WebOS, (21) Windows Mobile, (22) Windows Phone, (23)Adobe AIR, (24) Adobe Flash, (25) Adobe Shockwave, (26) Binary RuntimeEnvironment for Wireless (BREW), (27) Cocoa (API), (28) Cocoa Touch,(29) Java Platforms, (30) JavaFX, (31) JavaFX Mobile, (32) MicrosoftXNA, (33) Mono, (34) Mozilla Prism, XUL and XULRunner, (35) .NETFramework, (36) Silverlight, (37) Open Web Platform, (38) OracleDatabase, (39) Qt, (40) SAP NetWeaver, (41) Smartface, (42) Vexi, and(43) Windows Runtime.

In some embodiments, exemplary inventive computer-based systems of thepresent disclosure may be configured to utilize hardwired circuitry thatmay be used in place of or in combination with software instructions toimplement features consistent with principles of the disclosure. Thus,implementations consistent with principles of the disclosure are notlimited to any specific combination of hardware circuitry and software.For example, various embodiments may be embodied in many different waysas a software component such as, without limitation, a stand-alonesoftware package, a combination of software packages, or it may be asoftware package incorporated as a “tool” in a larger software product.

For example, exemplary software specifically programmed in accordancewith one or more principles of the present disclosure may bedownloadable from a network, for example, a website, as a stand-aloneproduct or as an add-in package for installation in an existing softwareapplication. For example, exemplary software specifically programmed inaccordance with one or more principles of the present disclosure mayalso be available as a client-server software application, or as aweb-enabled software application. For example, exemplary softwarespecifically programmed in accordance with one or more principles of thepresent disclosure may also be embodied as a software package installedon a hardware device.

In some embodiments, exemplary inventive computer-based systems of thepresent disclosure may be configured to handle numerous concurrent usersthat may be, but is not limited to, at least 100 (e.g., but not limitedto, 100-999), at least 1,000 (e.g., but not limited to, 1,000-9,999), atleast 10,000 (e.g., but not limited to, 10,000-99,999), at least 100,000(e.g., but not limited to, 100,000-999,999), at least 1,000,000 (e.g.,but not limited to, 1,000,000-9,999,999), at least 10,000,000 (e.g., butnot limited to, 10,000,000-99,999,999), at least 100,000,000 (e.g., butnot limited to, 100,000,000-999,999,999), at least 1,000,000,000 (e.g.,but not limited to, 1,000,000,000-10,000,000,000).

In some embodiments, exemplary inventive computer-based systems of thepresent disclosure may be configured to output to distinct, specificallyprogrammed graphical user interface implementations of the presentdisclosure (e.g., a desktop, a web app., etc.). In variousimplementations of the present disclosure, a final output may bedisplayed on a displaying screen which may be, without limitation, ascreen of a computer, a screen of a mobile device, or the like. Invarious implementations, the display may be a holographic display. Invarious implementations, the display may be a transparent surface thatmay receive a visual projection. Such projections may convey variousforms of information, images, and/or objects. For example, suchprojections may be a visual overlay for a mobile augmented reality (MAR)application.

In some embodiments, exemplary inventive computer-based systems of thepresent disclosure may be configured to be utilized in variousapplications which may include, but not limited to, gaming,mobile-device games, video chats, video conferences, live videostreaming, video streaming and/or augmented reality applications,mobile-device messenger applications, and others similarly suitablecomputer-device applications.

As used herein, the term “mobile electronic device,” or the like, mayrefer to any portable electronic device that may or may not be enabledwith location tracking functionality (e.g., MAC address, InternetProtocol (IP) address, or the like). For example, a mobile electronicdevice can include, but is not limited to, a mobile phone, PersonalDigital Assistant (PDA), Blackberry™, Pager, Smartphone, or any otherreasonable mobile electronic device.

As used herein, terms “proximity detection,” “locating,” “locationdata,” “location information,” and “location tracking” refer to any formof location tracking technology or locating method that can be used toprovide a location of, for example, a particular computingdevice/system/platform of the present disclosure and/or any associatedcomputing devices, based at least in part on one or more of thefollowing techniques/devices, without limitation: accelerometer(s),gyroscope(s), Global Positioning Systems (GPS); GPS accessed usingBluetooth™; GPS accessed using any reasonable form of wireless and/ornon-wireless communication; WiFi™ server location data; Bluetooth™ basedlocation data; triangulation such as, but not limited to, network basedtriangulation, WiFi™ server information based triangulation, Bluetooth™server information based triangulation; Cell Identification basedtriangulation, Enhanced Cell Identification based triangulation,Uplink-Time difference of arrival (U-TDOA) based triangulation, Time ofarrival (TOA) based triangulation, Angle of arrival (AOA) basedtriangulation; techniques and systems using a geographic coordinatesystem such as, but not limited to, longitudinal and latitudinal based,geodesic height based, Cartesian coordinates based; Radio FrequencyIdentification such as, but not limited to, Long range RFID, Short rangeRFID; using any form of RFID tag such as, but not limited to active RFIDtags, passive RFID tags, battery assisted passive RFID tags; or anyother reasonable way to determine location. For ease, at times the abovevariations are not listed or are only partially listed; this is in noway meant to be a limitation.

As used herein, terms “cloud,” “Internet cloud,” “cloud computing,”“cloud architecture,” and similar terms correspond to at least one ofthe following: (1) a large number of computers connected through areal-time communication network (e.g., Internet); (2) providing theability to run a program or application on many connected computers(e.g., physical machines, virtual machines (VMs)) at the same time; (3)network-based services, which appear to be provided by real serverhardware, and are in fact served up by virtual hardware (e.g., virtualservers), simulated by software running on one or more real machines(e.g., allowing to be moved around and scaled up (or down) on the flywithout affecting the end user).

In some embodiments, the exemplary inventive computer-basedsystems/platforms, the exemplary inventive computer-based devices,and/or the exemplary inventive computer-based components of the presentdisclosure may be configured to securely store and/or transmit data byutilizing one or more of encryption techniques (e.g., private/public keypair, Triple Data Encryption Standard (3DES), block cipher algorithms(e.g., IDEA, RC2, RCS, CAST and Skipjack), cryptographic hash algorithms(e.g., MD5, RIPEMD-160, RTRO, SHA-1, SHA-2, Tiger (TTH),WHIRLPOOL,RNGs).

The aforementioned examples are, of course, illustrative and notrestrictive.

As used herein, the term “user” shall have a meaning of at least oneuser. In some embodiments, the terms “user”, “subscriber” “consumer” or“customer” should be understood to refer to a user of an application orapplications as described herein and/or a consumer of data supplied by adata provider. By way of example, and not limitation, the terms “user”or “subscriber” can refer to a person who receives data provided by thedata or service provider over the Internet in a browser session, or canrefer to an automated software application which receives the data andstores or processes the data.

Applications based on techniques such as keypoint connect-the-dotskeletons superficially compute poses. Such applications can computelimited representations of human poses because most joints still haveone degree of freedom, the roll around their axis. For example, a bonestick figure does not indicate which way a head faces, or biomechanicalcharacteristics such as midriff twist or foot and wristsupination/pronation. This lack of joint angle information may constrainthe utility of this kind of estimation in real world applications.

Some embodiments of the present invention describe a system to localizehuman joints and solve for 3D human poses in terms of both position andfull 3-axis rotations, using at least one frame RGB monocular image. Insome embodiments, the system is enabled by a neural network detectorthat predicts the 3D location of a full set of keypoints by predictingsets of one dimensional heatmaps, significantly reducing the computationand memory complexity associated with volumetric heatmaps. Applicationsof the embodiments described herein include but are not limited to:person posture recognition (PPR) for postural ergonomic hazardassessment; enablement of low-cost motion capture freed from expensivestudios; improvements to Computer-Generated Imagery (CGI) and videogames animation, sports analysis and dynamic posture feedback,surveillance, medical applications and prognosis from physical movementanomalies, and human-computer interaction applications based on motionrecognition.

In some embodiments, a neural network detector determines 2D and 3D,keypoints related to the pose of a human from an image, image providingdepth information, or video. Such keypoints can then be post-processedto estimate the rotational pose of the human subject.

In some embodiments two feedforward neural networks can be implemented.For instance, a convolutional neural network for detection and aregression-based neural network with fully connected layers for addingdepth (‘lifting’) and refining a pose. Developing a model requiresidentifying and designing a suitable architecture, obtaining andpreparing useful data from which to learn, training the model with thedata, and validating the model.

Two types of keypoints are defined below, joint keypoints andorientation keypoints. Joint keypoints correspond to skeletal joints andin some instances, can include features such as eyes, ears, or nose.Orientation keypoints refer to a set or sets of arbitrary points rigidlyattached to a joint. They differ from dense pose correspondences in thatorientation keypoints do not correspond to a specific or recognizablebody part but instead are rigidly anchored in specific directions from ajoint (e.g., forward, or to a side). Orientation keypoints can beindependent of a body shape. In contrast to markers used in motioncapture orientation keypoints include a freedom feature i.e., they donot need to be on the body or a body part. For example, two sets oforientation keypoints can be assigned to the lower left leg, both setsmidway between knee and ankle, with one offset in a forward directionand another offset assigned to the outside (e.g., to the left for theleft leg).

In some embodiments multiple offsets can be used, for instance 0.5 bonelengths, which for the lower leg implies points well off the body. Bonelengths as a unit have the benefit of being independent of the size of asubject and can be customized to the size of each limb. For some smallerbones, the distance can be increased, for example, to reduce therelative significance of detection errors.

FIG. 1 illustrates an example of and implementation of neural networkdetector of orientation key points according to an illustrativeembodiment of the present disclosure. FIG. 1 conceptually illustratescomputing device for the implementation of a neural network detector oforientation keypoints. The compute device 100 can be a computer,smartphone, tablet, notepad and/or any other suitable electronic device.Such a compute device includes various types of processor-readable mediaand interfaces to operatively couple different types ofprocessor-readable media. Computing device 100 includes a bus 115, aprocessor 109, a system memory 103, a read-only memory 111, a storagedevice 101, an input device interface 113, an output device interface107, and a network communication interface 105.

The bus 115 collectively represents system, peripheral, and/or chipsetbuses that communicatively connect the numerous internal devices of thecompute device 100. For instance, the bus 115 communicatively connectsthe processor 109 with the read-only memory 111, the system memory 103,and the storage device 101. From these various memory units, theprocessor 109 can retrieve instructions to execute and/or data toprocess to perform the processes of the subject technology. Theprocessor 109 can be a single processor or a multi-core processor indifferent implementations. In some instances, the processor 109 can beany suitable processor such as, for example, a general-purposeprocessor, a field programmable gate array (FPGA), an applicationspecific integrated circuit (ASIC) and/or other suitable hardwaredevices.

The read-only memory (ROM) 111 stores static data and instructions thatare used by the processor 109 and/or other modules of the computedevice. The storage device 101 is a read-and-write memory device. Thisdevice is a non-volatile memory unit that stores instructions and dataeven when the compute device 100 is disconnected from power. In someimplementations, a mass-storage device (for example a magnetic oroptical disk and its corresponding disk drive) can be used as thestorage device 101. Other implementations can use removable storagedevices (for example a flash drive, or other suitable type of removablestorage devices) as the storage device 101.

Similar to the storage device 101, the system memory 103 can be aread-and-write memory device. Unlike storage device 101, however, thesystem memory 103 is a volatile read-and-write memory, such as arandom-access memory. The system memory 103 stores some of theprocessor-executable instructions and data that the processor 109 usesat runtime including processor-executable instructions to instantiateand maintain a neural network detector 117 and a three-axis jointrotation computing component 119 described below. Alternatively, theneural network detector and a three-axis joint rotation computing moduleor parts of the maintain a neural network detector 117 and a three-axisjoint rotation computing component 119 can reside in the storage device101. Accordingly, states and/or properties of an instance of the neuralnetwork detector 117 and a three-axis joint rotation computing component119 can prevail in non-volatile memory even when the compute device 100is disconnected from power. Thus, in some implementations, the front-endsynchronized application can be configured to automatically relaunch andsynchronize (if required) when the compute device 100 is reconnected topower. In such a case the detector system can execute according to thelast state of the neural network detector 117 and a three-axis jointrotation computing component 119 stored in the storage device 101 andsynchronization may be used for those elements the detector system thathave changed during the time the compute device 100 was turned off. Thisis an advantageous feature because instead of generating network trafficto synchronize all the elements of the neural network detector 117 and athree-axis joint rotation computing component 119 when the computingdevice is reconnected to power, only a subset of the elements may besynchronized and thus, in some instances some computational expense canbe avoided. In some implementations, local instances neural networkdetector 117 and a three-axis joint rotation computing component 119 canbe logically and operatively coupled.

In some embodiments, the executable instructions to run the processesdescribed herein on the computing device 100 can be stored in the systemmemory 103, permanent storage device 101, and/or the read-only memory111. For example, the various memory units can include instructions forthe computing of orientation keypoints including executable instructionsto implement a neural network detector 117 and three-axis joint rotationcomponent 119 in accordance with some implementations. For example, insome implementations, permanent storage device 101 can include processorexecutable instructions and/or code to cause processor 107 toinstantiate a local instance of the neural network detector 117operatively coupled to a local instance of a three-axis joint rotationcomponent 119. Processor executable instructions can further causeprocessor 207 to receive images or videos from non-local computingdevices not shown in FIG. 1.

In some embodiments the processor 109 coupled to one or more of memories103 and 111, and storage device 101 receive an image depicting at leastone subject. The processor can predict at least one orientation keypointassociated with a section of the body part of the at least one subjectvia a neural network detector 117 and compute a three-axis jointrotation via the three-axis joint rotation component. The orientationkeypoints can be associated with the section of the body part of the atleast one subject based on at least one orientation keypoint associatedwith the body part of the at least one subject and at least one jointkeypoint associated with the body part of the at least one subject.

In some embodiments the processor 109 coupled to one or more of memories103 and 111, and storage device 101 receive an image depicting at leastone subject. The processor can predict at least one orientation keypointassociated with a section of a body part of the at least one subject viathe neural network detector 117. Predict an aspect of a pose associatedwith the at least one subject based on the at least one orientationkeypoint, the aspect of the pose associated with the at least onesubject can include a position, size, and/or a movement associated withthe at least one subj ect.

In some implementations, the components 117 and 119 can be implementedin a general purpose and/or specialized processor (e.g., processor 109configured to optimize the tasks performed by these components). Inother implementations, the components shown in the processor 109 can beimplemented as a combination of hardware and software. For example, thestorage device 101 (or other suitable memory in the compute device 100)can include processor-executable instructions to render a graphicalrepresentation on a display comprising a plurality of marks indicativeof the three-axis joint rotation associated with the section of a bodypart of the at least one subject. Such a graphical representation isindicative of a pose of the at least one subject. The pose of thesubject can include one or more joint positions and at least one jointangle associated with the section of the body part.

The bus 115 also connects to the input device interface 113 and outputdevice interface 107. The input device interface 113 enables thecomputing device 100 to receive information or data, for example, imagesor video.

Output device interface 107 enables, for example, computed changes ofpositions one or more orientation keypoints over time. The orientationkeypoint can be computed to be outputted in a two-dimensional space orin a three-dimensional space. Likewise, the output device 107 can renderor output calculated rotational velocity or acceleration associated withone or more orientation keypoints based on changes in position of the atleast one orientation keypoint. Output devices used with output deviceinterface 107 can include, for example, printers, audio devices (e.g.,speakers), haptic output devices, and display devices (e.g., cathode raytubes (CRT), liquid crystal displays (LCD), gas plasma displays, touchscreen monitors, capacitive touchscreen and/or other suitable displaydevice). Some implementations include devices that function as bothinput and output devices (e.g., a touchscreen display).

As shown in FIG. 1, bus 115 can also couple compute device 100 to anetwork (not shown in FIG. 1) through a network communication interface105. In this manner, the computing device 100 can be part of a networkof computers (for example a local area network (“LAN”), a wide areanetwork (“WAN”), or an Intranet, or a network of networks, for examplethe Internet. Any or all components of computing device 100 can be usedin conjunction with the embodiments described herein.

FIG. 2 illustrates an example of detection of orientation keypointsaccording to an illustrative embodiment of the present disclosure.Circles located within the skeletal structure shown in FIG. 2 representjoint keypoints (e.g., joint keypoint 201). Orientation keypoints suchas 203 represent a forward direction from the center of a section of abody part. Orientation keypoints such as 205 represent an outwarddirection from the center of a section of a body part, for example, tothe left for left side limbs and trunk, and to the right for right sidelimbs. Orientation keypoints such as 207 represent an inward directionfrom the center of a section of a body part. Orientation keypoints suchas 209 represent a backward direction from the center of a section ofthe body part. Other directions that can be represented by orientationkeypoints can include higher direction from the center of a section ofthe body part, a lower direction from the center of a section of thebody part and/or other suitable direction with respect to the center ofa section of the body part. It is appreciated that orientation keypointscan be located outside the sections describing the skeletal model of asubject.

In some embodiments the system can be enabled at least in part via aneural network. A neural network is a computerized model including aseries of functions and operations which are combined to process andtransform input data into some form of output data. For example, somenetworks can be implemented to perform regression, others to performclassification, and yet others to effectively summarize data throughdimension reduction.

In some embodiments a neural network may be implemented with layers,where each layer can include multiple nodes which perform operations onthe inputs. Different nodes within a layer can vary from each other byusing different constants to, for example, multiply the inputs, and somemay only take a subset of the inputs from the previous layer. Differentlayers may perform different operations. The layers can then be stackedto perform these multiple operations in a series, with to generate afinal output.

In some embodiments the neural network can be trained by the repeateduse of data to discover parameters which best characterize a solution.For instance, a neural network can be trained using a supervisedlearning technique. Such a supervised learning technique uses the groundtruth, or known correct characterization, to guide the neural networklearning process by analyzing errors between the neural network outputsand the ground truth. For example, when predicting human poses fromimages, the neural network can use datasets which provide measurementsof the actual pose, as captured by specialized equipment.

In some embodiments training of the neural network can be based on anintermediate supervision technique. Such a technique provides guidancebased on the results from a middle stage in the neural network model'scalculation. Intermediate supervision can be used with the samesupervision signal in cases where the later stage further refines theresults. Alternatively, the intermediate signal can be a differenttarget to guide the model into first solving a related problem which maythen be useful in getting to the final predictions.

In some embodiments training of the neural network can be based onweakly supervised learning, using one or more metrics to providefeedback during training. For example, the greater availability of 2Dhuman pose annotation compared to 3D annotations can enable weaklysupervise training by re-projecting predicted 3D poses into 2D, andcomparing the reprojection to the 2D annotation.

In some embodiments one or more of supervised training, intermediatesupervision, weakly supervision individually, or in any combinationthereof can be employed. For instance, supervised training by itself,weakly supervised training by itself, or a combination of supervisedlearning with intermediate supervision.

In some embodiments each neural network node can be a function f (x)which transforms an input vector x into an output value. The inputvectors can have any number of elements, often organized in multipledimensions. A network chains different functions f, g, and h to producea final output y, where y=f (g(h(x))). As each intermediate layer canhave many nodes, the number of elements and input shapes can vary.

In some embodiments some functions computed within the neural networknode can include:

-   -   linear combinations—wherein nodes sum the inputs after        multiplying them by a fixed weight (i.e. the vector inner        product).    -   sigmoid or logistic function—these functions transform a single        input, usually the result of a linear combination node, into a        narrow range of −1 to 1, approaching the boundaries as the input        approaches +/− infinity. This is a non-linear and continuous        operation.    -   rectified linear units—these functions floor a value at zero,        eliminating negative results.

This creates a non-linearity at value zero.

-   -   max pooling—these functions take the maximum of the inputs and        can be used to reduce the number of neurons feeding into forward        layers through aggregation, and manage the computational cost.    -   normalization—there are a variety of normalization functions        including a type batch normalization, which transforms a batch        of different samples by the sample mean value and sample        standard deviation. Batch normalization can speed up training by        maintaining a broadly steady range of outputs even as the inputs        and weights change. For inference values can be frozen based on        the mean and standard deviation of the training set.    -   softmax layer—this technique rescales input neurons by taking        their exponential values and dividing by the sum of these        values. This means all values sum to one, approximating a        probability distribution. Due to the exponential function,        higher values will be accentuated and the resulting distribution        will be leptokurtic.

In some embodiments the neural network can be implemented as afeedforward neural network. In a feedforward neural network, the dataflows from the input to the output, layer by layer, without loopingback—i.e. the outputs of the neural network may not provide feedback forthe calculations. This flow is called a forward pass and depending onthe size of the neural network can represent millions of calculationsfor a single input sample.

In some embodiments loss refers to the amount of error in the neuralnetwork model, with the goal of learning generally to minimize the loss.There are many different measurements of loss. For regression relatedtasks, loss is most often the mean squared error. These measures, forsome sample of data, the average difference between the predicted valuesand the actual values, squared. Large outlier losses are particularlypenalized with this measure and its popularity stems from itssimplicity, mathematical convenience and prevalence in statisticalanalysis. Another alternative is the mean absolute error, which does nothighly weight large errors. The embodiments described herein can beimplemented using one or more loss functions including mean squarederror, mean absolute error, or other suitable loss function.

In some embodiments a stochastic gradient descent procedure can beapplied to converge toward an optimal solution. The method is stochasticbecause data is randomly shuffled and fed to the current state of aneural network model. The gradient is the partial derivative of theneural network model parameters and at each iteration the parameters canbe updated by a percentage of the gradient, i.e., the learning rate.Accordingly, the values of the parameters progress toward values whichminimize the loss for the training data at each repeated iteration.

In some embodiments the neural network model can be configured throughbackpropagation. This means that each time training data passes throughthe model, a function calculates a measure of loss based on theresulting predictions. From the resulting loss the gradient of the finallayer can then be derived, and consequently each previous layer'sgradient can be derived. This continues until the beginning of theneural network model and then the complete gradients are used to updatethe model weights like a stochastic gradient descent.

In some embodiments as a neural network model becomes deeper (manylayered) to handle more complicated analysis, training can becomeimpaired as the gradient of neurons in middle layers may approach zero.This can limit the ability of the neural network model to learn asweights cease to update when the gradient nears zero. This limitationcan be overcome by different techniques including Rectified Linear Units(ReLU). ReLUs are less susceptible to the vanishing gradient than otheractivation functions such as sigmoid, as the derivative only changeswhen the activation is negative. Rectified Linear Units can be used asthe principle activation function. Residual connections allow layers topass data forward and focus on modifying the data only by applyingadditive changes (i.e. residuals), and can be used to develop deepernetworks without vanishing gradients.

In some embodiments the neural network model can be implemented as aconvolutional neural network. Convolutional neural networks can exploitthe structure of an image to identify simple patterns, and then combinethe simple patterns into more complex ones. Each filter in aconvolutional neural network scans an adjacent area of the previouslayer, combining the values based on learned weights. The same filter,with the same weights, can then be slid across relevant dimensions tofind a pattern throughout an input. The filter generally penetrates thefull depth of a layer, recombining lower level features to expresshigher level features. The early levels of an image targeted convolutionnetwork typically find edges, then lines and then basic shapes likecorners and curves. This often means that the early layers of a trainedconvolutional neural network can be reused in other networks in aprocess called transfer learning.

FIGS. 3A-3C illustrate three joint keypoints, according to anillustrative embodiment of the present disclosure. The darker spots 301,303, and 305 each represent a joint keypoint localized by the system. Insome implementations heatmaps can be used as intermediate representationof an input of a convolutional neural network. In some instances,instead of directly predicting x and y coordinates of a feature, whichwould be a regression task, a prediction draws a point or blob in thepredicted location. The higher the value (‘heat’) for a given pixel themore likely the convolutional neural network model indicates that thefeature is centered in that position. Heatmaps can be trained by drawinga gaussian blob at the ground truth location, and predicted heatmaps canbe directly compared to these ground truth heatmaps. This techniqueallows a vision based network to remain in the vision space throughouttraining. For inference, either the pixel with the maximum value can beconverted to a location address (hardmax) or a probabilistic weight ofvalues can be converted to a blended address (soft argmax). An exampleof this technique is illustrated in FIGS. 3A-3C.

FIGS. 4A-4C illustrate three orientation keypoints, according to anillustrative embodiment of the present disclosure. The darker spots 401,403, and 405 each represents an orientation keypoint localized by theneural network. The darker spots represent a convolutional neuralnetwork model localizing a point.

In some embodiments the convolutional neural network can recover some ofthe resolution that can be lost on a heatmap. For example, additionaltechniques that can be used include 2D and 3D hardmax heatmaps. Thus, insome instances, results can be shifted by 0.25 pixels based on whichneighboring pixels has the next highest prediction. This technique caneffectively double the resolution in each direction. In some instances,during training, when generating target heatmaps symmetrical Guassianrounded to the nearest heatmap pixel may not be used, but insteadincreasing the resolution data in a more precise discrete sampling of aprobability distribution function when generating a target may beincorporated. This technique can allow an almost perfect reconstructionof a high-resolution location from the heatmap when using, for example,a spatial soft argmax layer.

In some embodiments various strategies and techniques can be used tomitigate overfitting of the neural network model. Some of thesetechniques include using diverse training data, data augmentation, earlystopping techniques, regularization, and dropout techniques. In someinstances, the neural network can be trained with more and diversetraining data to reduce overfitting. Data augmentation, is a techniquewhere various transformations are used on the available training data tosynthetically increase the size of the training data. Early stopping isthe process of stopping training when validation loss is no longerimproving, even if the training loss is still declining. Regularizationis a technique based on adding a penalty term to the loss based on themagnitude of the weights. Resulting in the learning of fewer or smallerweights and avoiding learning training noise. Dropout is a techniquewhich randomly shuts down different nodes during training iterations todevelop resilience and reduce the neural network model's ability to relyon specific idiosyncratic features which may not generalize well.

In some embodiments the neural network model can use or implement aPerspective-n-Point (PnP) technique. Perspective-n-Point is acomputation technique that takes a set of 2D image points and a rigid 3Dmodel to solve for the models transform in a camera frame. Threenon-co-linear points can be projected onto a 2D plane and can limit therotations to, for example, a maximum of four possibilities (‘P3P’). Afourth non-co-linear point can be used to calculate the rotation. Such aPnP technique can be used to compute the best fitting transform, forinstance, based on minimizing a reprojection error. Accordingly,three-axis joint rotations associated with subject's body part can bedetermined via a perspective-n-point computational technique.

In some embodiments rotations from images can be determined followingdetections by a convolutional neural network model. For instance, givenat least four keypoints identified in 2D space related to a singlejoint, 3D rotations can be determined using the convolutional neuralnetwork model.

In some embodiments, a variant of the P3P technique can be used whenfour points for each joint are given. In such a P3P variant the fourthpoint can be used to distinguish between subsequent orientationkeypoints per joint. For instance, to predict six orientation keypointsper joint, and implementation can use four sets of predictions and P4Pwith two bone ends and one orientation point for each set. Thereafter,the fourth set prediction can be averaged by using a quaternionaveraging algorithm. This technique can overweight bone endpointdetections in an overall solution.

In some embodiments a transform (rotation matrix, scale and translationvector) which minimizes the least square error between two sets ofpoints can be determined based on a set of estimated 3D points. Anoptimization equation can be determined based on, for example, aSingular Value Decomposition (SVD) technique to calculate the rotationmatrix and scale from the covariance matrix of the two sets of points,re-centered and normalized by the Froebenius norm. Such a technique canbe used to determine the transforms for individual comparisons, jointsets and batches of joint sets.

In some embodiments a full set of predicted orientation keypoints can bejointly transformed (with a single transformation matrix) to match theoriginal points. Thus, dealing with scaling—a monocular image may nothave scale or distance information—overcoming a setback to differentiatebetween an unusually large person at a distance and a small personpositioned closer to the camera.

In some embodiments the Procrustes transform can be used to find thebest fitting scale and distance values. The Procrustes transform canoptimally rotate a predicted skeleton relative to a camera to minimizepositional errors. Accordingly, three-axis joint rotations associatedwith a subject's body part can be computed via a Procrustescomputational technique. Likewise, a three-axis joint rotationassociated with a subject's body part can be computed via a Kabschcomputational technique. Likewise, a three-axis joint rotationassociated with a subject's body part can be determined via a regressioncomputational technique. In some implementations, three-axis jointrotations and the full rotation of a body part relative to a camera canbe computed using other techniques described herein.

As discussed above, in some embodiments a convolutional neural networkuse heatmaps to make positional or vector predictions, either through amax operation or a soft argmax integration. In some implementations 2Dheatmaps can be used to predict 2D points, which may square theresolution. In some instances, predicting 3D points with 3D heatmaps cancube the resolution, which can lead to a need of large memory andcalculation footprint during training and inference process. In such acase, increasing the resolution can become prohibitively expensive. Insome embodiments volumetric heatmaps can be replaced with three linearheatmaps, one for each axis. The application of such a techniqueachieves results with a small footprint, while enabling higherresolution. For instance, a 256 resolution heatmaps can be used instead64 resolution heatmaps resulting in a considerably sparser model. Insome implementations, more accurate results can be achieved, reducingthe dimensionality to a single dimension as opposed to solving for 3Dheatmaps using a pair of 2D heatmaps in the xy and yz.

FIG. 5 illustrates an example of a neural network detector according toan illustrative embodiment of the present disclosure. In FIG. 5 a Resnetbackbone is flattened from 3 to 2 dimensions and then convolutiontranspose layers can expand along a single dimension. Specifically, FIG.5 illustrates a 2D neural network detector with branches for X and Yhowever, Z can be included straightforwardly.

In some embodiments, the penultimate layer of the Resnet-50 backbone(501 in FIG. 5) can be sampled, a 2048 channel 8×8 tensor, into a 256channel 8×8 tensor using 1×1 kernel convolutions. Thereafter the tensorcan be flattened into the x-dimension with an 8×1 convolution, whichsliding along the x-dimension. From this flattened form, transposeconvolution layers can be used to upsample, for example, only the last,singular dimension. The number of upsamples can depend on a targetresolution. As this stage is inexpensive in one dimension, a 256resolution can be computed to match the initial image.

In some embodiments, a final 1×1 convolution layer can collapse thechannels into a 1D heatmap for each orientation keypoint along thesingle dimension. Each heatmap can represent a neural network's estimateof an orientation keypoint position along the single axis. The sametechnique can be applied for the Y dimension, flattening its own forkedhead from the Resnet backbone. The heatmaps can be visualized as 1dimensional scans in different directions across the image.

In some embodiments depth may not be a native dimension, thus the sameprinciple with a modified technique to flattening for depth can beapplied. For instance, by flattening the backbone, while adding a depthdimension, into a 256 channel 8×8×8 blocks. Thereafter, a convolutionlayer (8×8 kernel which may only slide in the depth dimension) cancollapse the first two dimensions of the 8×8×8 into 1×8 block. For depthcomputation, a double of the resolution can be used, depending on theangle of a camera and the position of the subject, the depth dimensionmay exceed the height and width. This characteristic may entail oneadditional convolution transpose layer. In some instances, the sameresolution can be preserved and rescaled the ground truth depth.

In some embodiments, a final number of heatmaps can double the number oforientation keypoints for 2D estimates and triple for 3D estimates, anincrease comparable to footprint savings of switching to linearresolution. One of the advantages of the embodiments disclosed herein isthat a meaningfully large reduction in multiply-add operation can beprocessed, particularly as the number of orientation keypointsincreases, when predicting 3D or when targeting a higher resolution.

In some embodiments a lifter/refiner can be implemented based on aregression model using fully connected ReLU layers. Likewise, such alifter/refiner can be implemented as a 1D convolutional network toinclude time 1D convolutions for video analysis. In someimplementations. The lifter/refiner can be used to process three-axisjoint rotations.

In some embodiments a neural network detector (e.g., a crosshairdetector or other suitable neural network detector) can make directpixel and/or voxel location predictions, sufficient to generatethree-axis joint rotations with the PnP based computational techniques,Procrustes based computational techniques, Kabsch based computationtechniques, and/or Singular-Value-Decomposition computationaltechniques. Such computational techniques can be executed by, forexample, the three-axis joint rotation component discussed in FIG. 1. Insome implementations the neural network can learn scales (e.g., humanscales) and convert them into real world dimension to make unadjustedpredictions. These techniques can be enabled via the lifter/refiner. Insome instances, the extra linear network can also focus further onoverall structure of the pose, learning sufficient information tocorrect individual predictions of the network. In some instances, thepose can include joint positions and and/or joint angles associated witha subject's body or section of the body.

In some embodiments the lifter/refiner can be implemented with a neuralnetwork inner block including a linear layer, followed by batchnormalization, dropout and rectified linear units. A neural-networkouter blocks can include two inner blocks and a residual connection. Aninitial linear layer can convert from the number of keypoint inputs,flattened to a single dimension, into the linear width of the network. Afinal layer can convert from the width to the number of predictions. Insome instances, substantial gains can be achieved from widening theneural network, for example, by increasing the size of each linear layerby 50% from 1024 to 1536, which approximately can double the totalparameters. This helps to accommodate 3-5× as many keypoint inputs andoutputs from incorporating orientation keypoints.

In some embodiments, the lifter/refiner may be implemented without usingdepth predictions form a detector model. In some instances, 2D pointscan provide sufficient information for the lifter/refiner to make moreaccurate, refined predictions of depth position.

In some embodiments the neural network can be trained with a dataset(e.g., MPII dataset). Such a dataset MPII can act as a regulizer againstthe narrowness characteristic of other datasets. Using the dual datasettechnique for training, can prevent the neural network model fromoverfitting the training subjects/samples and plateauing when faced withthe validation subjects/samples.

In some embodiments the neural network model can be trained usingmultiple types of datasets including but not limited to MPII, DensePose,Surreal, ImageNet, COCO, HumanEva, and Pnoptic. Preprocessing techniquescan include configuring a layer of processing in bulk to extract themost relevant parts of the dataset while avoiding unnecessary repeatedcalculations during training. Likewise, preprocessing techniques caninclude preprocessing at the time of training mainly as a form of dataaugmentation and/or to tailor the data to the needs of animplementation.

In some embodiments a layer of processing in bulk is applied to extractthe most relevant parts of a dataset while avoiding unnecessary repeatedcalculations during training. Thereafter a subsequent technique can beapplied including preprocessing at the time of training as a form ofdata augmentation or to tailor the data to the needs of animplementation.

In some embodiments orientation keypoints add points which may lieoutside the silhouette of a subject and outside a tighter fittingbounding box. In some instances, an affine transformation of thekeypoints, can be applied to ensure most keypoints can be mapped toviable heatmap coordinates, shifting and shrinking their coordinates bye.g., 20% to fit within the resolution of an image. Thus, each heatmapcan cover a wider area than the image itself. This technique canmaximize the resolution of the visual clues in an image while stillbeing able to predict the orientation keypoints outside a silhouette.

In some embodiments a transfer learning training technique can be usedto provide the initial backbone of a neural network. In some instances,the last layers of the neural network model can be configured to befocused on the classification task. In some implementations, most of theneural network model can use convolutional layers to filter and siftthrough visual clues in an image. In some instances, some limitations ofusing some types of datasets (e.g., Human3.6 million dataset) can bemitigated by using the earlier layers pretrained on a diverse dataset.Thereafter, the neural network model can be attached to the output ofthe Resnet layers to learn keypoint localization.

In some embodiments the neural network model can process a loss functionbased on mean square error during the training phase of the neuralnetwork. Likewise, other types loss functions such as a loss functionbased on absolute error can also be used during the training phase. Forexample, a mean square function can be initially used to quickly trainthe head of a neural network before switching to a loss function basedon absolute error for further finetuning. In some implementations,various balances of the weights between the loss functions of differentdatasets can be applied. For instance, the loss on the Human3.6 datasetand MPII dataset can be settled on 0.75/0.25 as shown in the equationbelow:

Loss_(dual)=0.75Loss H3.6 m_(77kps)+0.25Loss MPII_(visibleykps)*Magnitude Adj

In some embodiments the neural network detector can be trained usingmini-batches of 32 images with 16 from each dataset. This technique caninclude freezing the Resnet and train for 25 k iterations using L2 losswith a 0.001 learning rate. Thereafter, the technique can proceed toswitching to L1 and unfreeze the last layer group of the Resnet backboneand train for another 25k iterations, before dropping the learning ratefor 10k iterations to 0.0005 and 15k iterations at 0.00025. Followed byunfreezing the penultimate layer of the Resnet backbone for finalfine-tuning and training for another 25 k iterations.

FIGS. 6A-B illustrates an example of detection of orientation keypointsaccording to an illustrative embodiment of the present disclosure. FIG.6A shows ah image 601 with ground truth data 603 and predictions enabledby the described embodiments including joint predictions, posepredictions, and rotation angles. Handles such as 607, shown in FIG. 6B,indicate a forward orientation while handles such as 609 indicate a leftorientation. At least some aspects of the present disclosure will now bedescribed with reference to the following numbered clauses.

-   1. An apparatus, comprising:    -   a processor; and    -   a memory storing instruction which, when executed by the        processor, causes the processor to:        -   receive an image depicting at least one subject;        -   predict at least one orientation keypoint associated with a            section of the body part of the at least one subject via a            neural network detector; and        -   compute a three-axis joint rotation associated with the            section of the body part of the at least one subject based            on at least one orientation keypoint associated with the            body part of the at least one subject and at least one joint            keypoint associated with the body part of the at least one            subject.-   2. The apparatus of claim Error! Reference source not found. wherein    the memory storing instructions which, when executed by the    processor, further causes the processor to:    -   render a graphical representation on a display comprising a        plurality of marks indicative of the three-axis joint rotation        associated with the section of the body part of the at least one        subject.-   3. The apparatus of claim 1, wherein the graphical representation is    indicative of a pose of the at least one subject.-   4. The apparatus of claim 1, wherein the pose of the at least one    subject comprises at least one joint position and at least one joint    angle associated with the section of the body part of the at least    one subject.-   5. The apparatus of claim 1, wherein the memory storing instructions    which, when executed by the processor, further causes the processor    to:    -   train a neural network based on an orientation key point from        the at least one orientation keypoint.-   6. The apparatus of claim 1, wherein the memory storing instructions    which, when executed by the processor, further causes the processor    to:    -   produce training data for a neural network to estimate a subject        pose.-   7. The apparatus of claim 1, wherein the memory storing instructions    which, when executed by the processor, further causes the processor    to:    -   predict the at least one subject pose in a three-dimensional        space base on an estimated two-dimensional position associated        with the at least one orientation keypoint.-   8. The apparatus of claim 1, wherein the memory storing instructions    which, when executed by the processor, further causes the processor    to:    -   compute a change in position of the at least one orientation        keypoint over time; and    -   calculate a rotational velocity or acceleration associated with        the at least one orientation keypoint based on the change in        position of the at least one orientation keypoint.-   9. The apparatus of claim Error! Reference source not found.,    wherein the at least one orientation keypoint is predicted in a    two-dimensional space.-   10. The apparatus of claim 1, wherein the at least one orientation    keypoint is predicted in a three-dimensional space.-   11. The apparatus of claim 1, wherein the image is produced by at    least one of a camera and a video camera.-   12. The apparatus of claim Error! Reference source not found.,    wherein the images comprises depth information.-   13. The apparatus of claim Error! Reference source not found.,    wherein the at least one orientation keypoint is located outside the    section of the body part of the at least one subject and is    indicative of a kinematic rotation.-   14. The apparatus of claim 1, where in the kinematic rotation    comprises at least one of a forward direction from the center of the    section of the body part, an outward direction from the center of    the section of the body part, an inward direction from the center of    the section of the body part, a backward direction from the center    of the section of the body part, and a lower direction from the    center of the section of the body part.-   15. The apparatus of claim Error! Reference source not found.,    wherein the three-axis joint rotation associated with the section of    the body part of the at least one subject is determined via a    perspective-n-point computational technique.-   16. The apparatus of claim Error! Reference source not found.,    wherein the three-axis joint rotation associated with the section of    the body part of the at least one subject is determined via a    Procrustes computational technique.-   17. The apparatus of claim 1, wherein the three-axis joint rotation    associated with the section of the body part of the at least one    subject is determined via a Kabsch computational technique.-   18. The apparatus of claim 1, wherein the three-axis joint rotation    associated with the section of the body part of the at least one    subject is determined via a regression technique.-   19. An apparatus, comprising:    -   a processor; and    -   a memory storing instruction which, when executed by the        processor, causes the processor to:        -   receive an image depicting at least one subject;        -   predict at least one orientation keypoint associated with a            section of a body part of the at least one subject via the            neural network detector; and        -   predict an aspect of a pose associated with the at least one            subject based on the at least one orientation keypoint, the            aspect of the pose associated with the at least one subject            comprises at least one of a position associated with the at            least one subject, size associated with at least one            subject, and a movement associated with the at least one            subject.-   20. The apparatus of claim 19, wherein the pose of the at least one    subject comprises at least one joint position and at least one joint    angle associated with the section of the body part of the at least    one subject.-   21. The apparatus of claim 19, wherein the at least one orientation    keypoint is predicted in a two-dimensional space.-   22. The apparatus of claim 19, wherein at least one orientation    keypoint is used for training or supervising a network or model.-   23. The apparatus of claim 19, wherein the memory storing    instructions which, when executed by the processor, further causes    the processor to:    -   train a neural network based on an orientation key point from        the at least one orientation keypoint.-   24. The apparatus of claim 19, wherein the memory storing    instructions which, when executed by the processor, further causes    the processor to:    -   predict the at least one subject pose in a three-dimensional        space base on an estimated two-dimensional position associated        with the at least one orientation keypoint.-   25. The apparatus of claim 19, wherein the memory storing    instructions which, when executed by the processor, further causes    the processor to:    -   compute a change in position of the at least one orientation        keypoint over time; and    -   compute at least one of a rotational velocity associated with        the at least one orientation keypoint and acceleration        associated with the at least one orientation keypoint based on        the change in position of the at least one orientation keypoint.-   26. A method, comprising:    -   receiving an image depicting at least one subject;        -   predicting at least one orientation keypoint associated with            a section of the body part of the at least one subject via a            neural network detector; and        -   computing a three-axis joint rotation associated with the            section of the body part of the at least one subject based            on at least one orientation keypoint associated with the            body part of the at least one subject and at least one joint            keypoint associated with the body part of the at least one            subject.-   27. The method of claim 20, wherein the method further comprises:    -   rendering a graphical representation on a display comprising a        plurality of marks indicative of the three-axis joint rotation        associated with the section of the body part of the at least one        subject.-   28. The method of claim Error! Reference source not found., wherein    the graphical representation is indicative of a pose of the at least    one subject.-   29. The method of claim 20, wherein the pose of the at least one    subject comprises at least one joint position and at least one joint    angle associated with the section of the body part of the at least    one subject.-   30. The method of claim 20, wherein the method further comprises:    -   training a neural network based on an orientation key point from        the at least one orientation keypoint.-   31. The method of claim 20, wherein the method further comprises:    -   producing training data for a neural network to estimate a        subject pose.-   32. The method of claim 20, wherein the method further comprises:    -   predicting the at least one subject pose in a three-dimensional        space base on an estimated two-dimensional position associated        with the at least one orientation keypoint.-   33. The method of claim 20, wherein the method further comprises:    -   computing a change in position of the at least one orientation        keypoint over time; and    -   calculating a rotational velocity or acceleration associated        with the at least one orientation keypoint based on the change        in position of the at least one orientation keypoint.-   34. The method of claim 20, wherein the at least one orientation    keypoint is predicted in a two-dimensional space.-   35. The method of claim 20, wherein the at least one orientation    keypoint is predicted in a three-dimensional space.-   36. The method of claim 20, wherein the image is produced by at    least one of a camera and a video camera.-   37. The method of claim 20, wherein the images comprises depth    information.-   38. The method of claim 20, wherein the at least one orientation    keypoint is located outside the section of the body part of the at    least one subject and is indicative of a kinematic rotation.-   39. The method of claim 20, where in the kinematic rotation    comprises at least one of a forward direction from the center of the    section of the body part, an outward direction from the center of    the section of the body part, an inward direction from the center of    the section of the body part, a backward direction from the center    of the section of the body part, and a lower direction from the    center of the section of the body part.-   40. The method of claim 20, wherein the three-axis joint rotation    associated with the section of the body part of the at least one    subject is determined via a perspective-n-point computational    technique.-   41. The method of claim 20, wherein the three-axis joint rotation    associated with the section of the body part of the at least one    subject is determined via a Procrustes computational technique.-   42. The method of claim 20, wherein the three-axis joint rotation    associated with the section of the body part of the at least one    subject is determined via a Kabsch computational technique.-   43. The method of claim 20, wherein the three-axis joint rotation    associated with the section of the body part of the at least one    subject is determined via a regression technique.-   44. A method, comprising:    -   receiving an image depicting at least one subject;    -   predicting at least one orientation keypoint associated with a        section of a body part of the at least one subject via the        neural network detector; and    -   predicting an aspect of a pose associated with the at least one        subject based on the at least one orientation keypoint, the        aspect of the pose associated with the at least one subject        comprises at least one of a position associated with the at        least one subject, size associated with at least one subject,        and a movement associated with the at least one subject.-   45. The method of claim 44, wherein the pose of the at least one    subject comprises at least one joint position and at least one joint    angle associated with the section of the body part of the at least    one subject.-   46. The method of claim 44, wherein the at least one orientation    keypoint is predicted in a two-dimensional space.-   47. The method of claim 44, wherein at least one orientation    keypoint is used for training or supervising a network or model-   48. The method of claim 44, wherein the method further comprises:    -   training a neural network based on an orientation key point from        the at least one orientation keypoint.-   49. The method of claim 44, wherein the method further comprises:    -   predict an object pose in a three-dimensional space base on an        estimated two-dimensional position associated with the at least        one orientation keypoint.-   50. The method of claim 44, wherein the method further comprises:    -   computing a change in position of the at least one orientation        keypoint over time; and    -   computing at least one of a rotational velocity associated with        the at least one orientation keypoint and acceleration        associated with the at least one orientation keypoint based on        the change in position of the at least one orientation keypoint.

Publications cited throughout this document are hereby incorporated byreference in their entirety. While one or more embodiments of thepresent disclosure have been described, it is understood that theseembodiments are illustrative only, and not restrictive, and that manymodifications may become apparent to those of ordinary skill in the art,including that various embodiments of the inventive methodologies, theinventive systems/platforms, and the inventive devices described hereincan be utilized in any combination with each other. Further still, thevarious steps may be carried out in any desired order (and any desiredsteps may be added and/or any desired steps may be eliminated).

1. An apparatus, comprising: a processor; and a memory storinginstruction which, when executed by the processor, causes the processorto: receive an image depicting at least one subject; predict at leastone orientation keypoint associated with a section of the body part ofthe at least one subject via a neural network detector; and compute athree-axis joint rotation associated with the section of the body partof the at least one subject based on at least one orientation keypointassociated with the body part of the at least one subject and at leastone joint keypoint associated with the body part of the at least onesubject.
 2. The apparatus of claim 1, wherein the memory storinginstructions which, when executed by the processor, further causes theprocessor to: train a neural network based on an orientation key pointfrom the at least one orientation keypoint.
 3. The apparatus of claim 1,wherein the memory storing instructions which, when executed by theprocessor, further causes the processor to: produce training data for aneural network to estimate a subject pose.
 4. The apparatus of claim 1,wherein the memory storing instructions which, when executed by theprocessor, further causes the processor to: predict the at least onesubject pose in a three-dimensional space base on an estimatedtwo-dimensional position associated with the at least one orientationkeypoint.
 5. The apparatus of claim 1, wherein the memory storinginstructions which, when executed by the processor, further causes theprocessor to: compute a change in position of the at least oneorientation keypoint over time; and calculate a rotational velocity oracceleration associated with the at least one orientation keypoint basedon the change in position of the at least one orientation keypoint. 6.The apparatus of claim 1, wherein the at least one orientation keypointis predicted in at least one of a two-dimensional space and athree-dimensional space.
 7. The apparatus of claim 1, wherein the imageis produced by at least one of a camera and a video camera.
 8. Theapparatus of claim 1, wherein the image comprises depth information. 9.The apparatus of claim 1, wherein the at least one orientation keypointis located outside the section of the body part of the at least onesubject and is indicative of a kinematic rotation.
 10. The apparatus ofclaim 1, where in the kinematic rotation comprises at least one of aforward direction from the center of the section of the body part, anoutward direction from the center of the section of the body part, aninward direction from the center of the section of the body part, abackward direction from the center of the section of the body part, anda lower direction from the center of the section of the body part. 11.The apparatus of claim 1, wherein the three-axis joint rotationassociated with the section of the body part of the at least one subjectis determined via a at least one of a perspective-n-point computationaltechnique, a Procrustes computational technique, a Kabsch computationaltechnique, and a regression technique.
 12. An apparatus, comprising: aprocessor; and a memory storing instruction which, when executed by theprocessor, causes the processor to: receive an image depicting at leastone subject; predict at least one orientation keypoint associated with asection of a body part of the at least one subject via the neuralnetwork detector; and predict an aspect of a pose associated with the atleast one subject based on the at least one orientation keypoint, theaspect of the pose associated with the at least one subject comprises atleast one of a position associated with the at least one subject, sizeassociated with at least one subject, and a movement associated with theat least one subj ect.
 13. The apparatus of claim 12, wherein the poseof the at least one subject comprises at least one joint position and atleast one joint angle associated with the section of the body part ofthe at least one subject.
 14. The apparatus of claim 12, wherein the atleast one orientation keypoint is predicted in at least one of atwo-dimensional space and a three-dimensional space.
 15. The apparatusof claim 12, wherein at least one orientation keypoint is used fortraining or supervising a network or model.
 16. The apparatus of claim12, wherein the memory storing instructions which, when executed by theprocessor, further causes the processor to: train a neural network basedon an orientation key point from the at least one orientation keypoint.17. The apparatus of claim 12, wherein the memory storing instructionswhich, when executed by the processor, further causes the processor to:predict the at least one subject pose in a three-dimensional space baseon an estimated two-dimensional position associated with the at leastone orientation keypoint.
 18. The apparatus of claim 12, wherein thememory storing instructions which, when executed by the processor,further causes the processor to: compute a change in position of the atleast one orientation keypoint over time; and compute at least one of arotational velocity associated with the at least one orientationkeypoint and acceleration associated with the at least one orientationkeypoint based on the change in position of the at least one orientationkeypoint.
 19. A method, comprising: receiving an image depicting atleast one subject; predicting at least one orientation keypointassociated with a section of the body part of the at least one subjectvia a neural network detector; and computing a three-axis joint rotationassociated with the section of the body part of the at least one subjectbased on at least one orientation keypoint associated with the body partof the at least one subject and at least one joint keypoint associatedwith the body part of the at least one subject.
 20. The method of claim19, wherein the method further comprises: rendering a graphicalrepresentation on a display comprising a plurality of marks indicativeof the three-axis joint rotation associated with the section of the bodypart of the at least one subject.